Essentials to Help Ensure Operational Efficiency
Forecasting demand can be one of the trickiest parts of managing a business. From juggling seasonal patterns to accounting for unknown circumstances, you may feel like you need a crystal ball to gauge today’s ever-changing landscape.
Even the most seasoned business leaders and inventory experts can get into trouble if they overlook key factors or rely too heavily on outdated methods. Inaccurate forecasting can lead to operational inefficiencies, stockouts, and high carrying costs, affecting customer retention as well as your bottom line.
We’re here to help you identify the critical components of demand forecasting, so you can avoid common mistakes and refine your logistics operations.
All forecasting methodologies are null without the data to power them. As the most powerful tool at your disposal, the way you gather and process data is essential to successfully predicting inventory levels. Something as small as duplicate entries, line errors, or formatting mistakes can throw your entire model out of line.
Data segmentation is also often overlooked in demand forecasting. For example, distinguishing between states may not be enough when predicting stocking levels for snow tires for the month of January. Dividing your data deeper into counties and sub-regions would give you a clearer understanding of historical demand.
Finally, utilizing historical data can help you gauge what’s currently on the horizon. Not only should you base your models on concrete numbers, but combing through past projections and identifying pitfalls in previous forecasts can help you refine your methodology while moving closer to your inventory goals.
From time series to Poisson Models to Exponential Smoothing, there are countless ways to model forecasting, but they are not all alike. Exponential Smoothing and Croston’s Method are some of the most common forecasting models, but they rely heavily on a normal distribution of demand. If your demand is more sporadic or seasonal, this modelling process will overinflate your inventory count and leave you with high carrying costs. Some models, like Reliability Models, are expansive and can gauge stockouts and replacements for intricate supply chains. However, they require vast amounts of data to predict these outcomes and are often tricky to manage.
From simple to complex, each model has its own unique capabilities. Taking the time to explore your forecasting needs and assessing which models best fit your demand timeline will save you time and money in the end. It is essential to have a basic understanding of your inventory flows before choosing a model, or you risk overcomplicating existing processes.
Many inventory managers find an acceptable forecasting model and call it a day. This “set it and forget it” mentality can yield wildly inaccurate results. To successfully forecast inventory, you should get in the practice of running models at a regular cadence. Remember, industry trends and current events can drastically alter purchasing habits. To stay on top of projected demand, stay ahead of potential inventory risks, and build confidence in your projections, it’s best to run models at least quarterly.
Reliance on old technologies may be the single most significant inhibitor to effective demand planning. Legacy methods like Excel require time-consuming manual data entry that cannot react quickly to changing trends. New demand planning software like Epicor IP&O can automate processes that previously took hours to create, improving operational efficiency and boosting performance levels. By being smart with your data collection, identifying the right models to fit your needs, staying ahead of consumer trends, and letting technology do the heavy lifting, you can realize the full benefits of demand forecasting across your enterprise.
Learn more about how Epicor IP&O can help your business thrive.
